John Jay College of Criminal Justice Bloodhounds
Also known as: John Jay College of Criminal Justice Bloodhounds
Program History
Model Outputs
2021-2022
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 996 (#348) | HCA +115 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | -19.4 (#777) | HCA +2.7 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -20.1 (#572) | HCA +2.4 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.432 (#510) | AdjO 67.5 | AdjD 70.5 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.201 (#542) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.243 (#541) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 898 (#481) | RD 350 | GP 1 |
2022 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2021-12-12 | @ | Hofstra Pride | L | 51 - 102 |
2022 Roster
Minutes by Position
The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.
| Player | Pos | GP | MIN | PTS | REB | AST | STL | BLK | TO | FGA | Numbers | PM | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C. Jones Jr.
|
- | 1 | 21.0 | 10.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 8.0 | 5.0 | -50 | -3.3 | -4.9 | 37.5 | 33.3 | 100.0 | 0.49 | 56.3 | 50.0 |
F. Barbato
|
- | 1 | 13.0 | 8.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 6.0 | - | - | - | 75.0 | 100.0 | 0 | - | 100.0 | 100.0 |
G. Watkins
|
- | 1 | 20.0 | 6.0 | 1.0 | 1.0 | 0.0 | 0.0 | 3.0 | 11.0 | -6.0 | - | - | - | 27.3 | 0.0 | 0 | - | 27.3 | 27.3 |
TJ Chisolm
|
- | 1 | 13.0 | 6.0 | 0.0 | 4.0 | 1.0 | 0.0 | 1.0 | 7.0 | 3.0 | - | - | - | 42.9 | 0.0 | 0 | - | 42.9 | 42.9 |
M. Leston
|
- | 1 | 12.0 | 4.0 | 1.0 | 1.0 | 1.0 | 0.0 | 3.0 | 5.0 | -1.0 | - | - | - | 40.0 | 0.0 | 0.0 | - | 36.8 | 40.0 |
A. Rivera
|
- | 1 | 23.0 | 4.0 | 0.0 | 0.0 | 1.0 | 0.0 | 4.0 | 9.0 | -8.0 | 26 | 1.0 | 1.6 | 22.2 | 0.0 | 0 | 0.29 | 22.2 | 22.2 |
J. Fraser
|
- | 1 | 12.0 | 4.0 | 5.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 | 6.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
I. Holmes
|
- | 1 | 16.0 | 3.0 | 2.0 | 0.0 | 1.0 | 0.0 | 3.0 | 5.0 | -2.0 | - | - | - | 20.0 | 20.0 | 0 | 0.1 | 30.0 | 30.0 |
J. Alleyne
|
- | 1 | 13.0 | 2.0 | 4.0 | 0.0 | 1.0 | 0.0 | 1.0 | 4.0 | 2.0 | - | - | - | 25.0 | 0.0 | 0 | - | 25.0 | 25.0 |
C. Braat
|
- | 1 | 14.0 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 | 1.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
J. Bell
|
- | 1 | 13.0 | 2.0 | 5.0 | 3.0 | 0.0 | 0.0 | 3.0 | 2.0 | 5.0 | -80 | -2.8 | -8.1 | 50.0 | 0 | 0 | -0.43 | 50.0 | 50.0 |
C. Dicintio
|
- | 1 | 9.0 | 0.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
J. Morales
|
- | 1 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
D. Kenny
|
- | 1 | 11.0 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 | 2.0 | 1.0 | 1.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
Numbers/Game vs RAPM
X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.
Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG